Pattern recognition machines such as the neural network machine: described in U.S. Pat. No. 5,067,164, issued Nov. 19, 1991, and assigned to Applicants' assignee, are finding increasing use in the rapid and automatic identification of unknown alphanumeric and other symbols or images. In these applications, the images of an unknown pattern such as a string of unknown handwritten characters first are segmented in accordance with some segmentation scheme designed to isolate the strokes which comprise the individual characters of the string. Following segmentation, the segments are analyzed to determine their most likely identity.
In most prior systems, these segments are required to be a defined "correct" set of segments of the image, before the recognition system is queried. Requiring a correct segment set puts severe constraints on the performance of the segmentation system, however, because an incorrect segmentation will almost invariably result in an incorrect solution to the recognition problem. A method of calling the recognizer which relaxes this constraint, but which at the same time is time-efficient in that the number of "calls" to the recognition engine is kept at a minimum, would be advantageous in terms of overall system performance.
A related problem is that current systems which are designed to recognize whole words, but which are based on single character recognition systems, require that the image data be segmented beforehand, usually manually. This is a very time consuming and expensive process.
Additionally, many prior recognition systems are highly problem-dependent, in that they use many ad hoc heuristics specific to the recognition tasks at hand, and require many parameters which must be carefully tuned for the problem at hand. This can result in systems which become very hard to improve, due to the highly complex interaction between all the parameters. Such systems also are very hard if not impossible to adapt to slightly different recognition tasks, because of the specific nature of the heuristics used.
An additional aspect of prior art recognition systems is that the recognition problem is often not considered in isolation, but is instead constrained to lie within bounds imposed by a lexicon. Prior systems often use a lexicon to veto recognition results as a post-processing step. A system which could readily incorporate small lexicons (which themselves may be dynamic, in that they may be generated by some other part of a larger system), and which returns the lexicon with a score attached to each entry, would improve recognition machine performance.